Function conv2DBackpropInputImpl

tfjs-node/src/kernels/Conv2DBackpropInput.ts:40–69

(
    dy: TensorInfo, filter: TensorInfo, convInfo: backend_util.Conv2DInfo,
    backend: NodeJSKernelBackend)

};

function conv2DBackpropInputImpl(
    dy: TensorInfo, filter: TensorInfo, convInfo: backend_util.Conv2DInfo,
    backend: NodeJSKernelBackend): Tensor4D {
  if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
    throw new Error(
        `TF Backend supports only 'valid' and 'same' padding ` +
        `while padding was ${convInfo.padInfo.type}`);
  }
  const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
  const padding = convInfo.padInfo.type;
  const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
  const dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
  const opAttrs = [
    createTensorsTypeOpAttr('T', 'float32'),
    {name: 'strides', type: backend.binding.TF_ATTR_INT, value: strides},
    {name: 'padding', type: backend.binding.TF_ATTR_STRING, value: padding}, {
      name: 'data_format',
      type: backend.binding.TF_ATTR_STRING,
      value: dataFormat
    },
    {name: 'use_cudnn_on_gpu', type: backend.binding.TF_ATTR_BOOL, value: true},
    {name: 'dilations', type: backend.binding.TF_ATTR_INT, value: dilations}
  ];
  const inputSizes = tensor1d(convInfo.inShape, 'int32');
  const res =
      backend.executeSingleOutput(
          Conv2DBackpropInput, opAttrs, [inputSizes, filter, dy]) as Tensor4D;
  inputSizes.dispose();
  return res;
}